{"id":"W4381734343","doi":"10.1109/noms56928.2023.10154385","title":"5G Network Slice Type Classification using Traditional and Incremental Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Quality of service; Slicing; Computer network; Cellular network; Distributed computing; Stochastic gradient descent; Heterogeneous network; Radio access network; Artificial intelligence; Machine learning; Wireless network; Artificial neural network; Mobile station; Wireless; Base station; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002419522,0.00006537791,0.00006415901,0.00004565425,0.0002275603,0.0001076155,0.0001394763,0.00003773204,0.00003074983],"category_scores_gemma":[0.00001997192,0.00006232131,0.00001632111,0.0006525001,0.00002024118,0.0002318863,0.00005152214,0.00009771273,0.00005467928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000215352,"about_ca_system_score_gemma":0.00002473553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002267915,"about_ca_topic_score_gemma":0.000004042716,"domain_scores_codex":[0.9993135,0.0000386828,0.0001051459,0.0002109326,0.0001471767,0.0001846031],"domain_scores_gemma":[0.999639,0.0001369825,0.00003683421,0.0001090304,0.0000278578,0.00005024997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003084607,0.00009290258,0.1072141,0.00003179945,0.00008575901,0.0000303351,0.001137688,0.1124535,0.007358334,0.5997868,0.06927592,0.1025019],"study_design_scores_gemma":[0.000136753,0.00004760168,0.07154302,0.00001336171,0.000003738401,0.00001469545,0.00004836024,0.9216895,0.00002844023,0.003122657,0.003236813,0.0001150673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6683704,0.0001616997,0.321236,0.000899985,0.0009149018,0.0001513261,8.903825e-7,0.001110595,0.00715422],"genre_scores_gemma":[0.9797009,0.0000417755,0.01937508,0.0002666691,0.0002894169,0.000003006649,0.00001871539,0.000007163498,0.0002973111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.809236,"threshold_uncertainty_score":0.2541389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1147769906839612,"score_gpt":0.2744998654800023,"score_spread":0.159722874796041,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}